Techniques for assessing a computing risk of an entity based on a social media footprint of that user. Social media accounts of an entity are identified. Queries are executed against the accounts to determine a social media footprint the entity has based on engagements with those accounts. The footprint reflects at least how much information associated with the entity has been exposed as a result of the engagements with those accounts. Based on the footprint, a risk score for the entity is developed. The risk score reflects a determined amount of computing risk the entity has with regard to potential malicious use of exposed entity information. Based on the risk score, mitigation operations are performed in an attempt to reduce the risk score.

Techniques for assessing a computing risk of an entity based on a social media footprint of that user. Social media accounts of an entity are identified. Queries are executed against the accounts to determine a social media footprint the entity has based on engagements with those accounts. The footprint reflects at least how much information associated with the entity has been exposed as a result of the engagements with those accounts. Based on the footprint, a risk score for the entity is developed. The risk score reflects a determined amount of computing risk the entity has with regard to potential malicious use of exposed entity information. Based on the risk score, mitigation operations are performed in an attempt to reduce the risk score.

A POI popularity derivation device (10) includes: a dictionary generation unit (11) that assigns a feature word used as a co-occurrence word of a POI name to each popularity-assigned POI name serving as a popularity assignment target to generate a popularity-assigned POI dictionary in which a popularity-assigned POI name and a feature word are associated with each other; an extraction unit (12) that extracts posted data serving as a search target from posted data on the basis of predetermined criteria; and a popularity derivation unit (18) that searches for the posted data on the basis of a predetermined rule regarding feature words while referring to the popularity-assigned POI dictionary, to extract posted data linked to the popularity-assigned POI name, and derives the popularity of each popularity-assigned POI name on the basis of the number of pieces of extracted posted data for each popularity-assigned POI name.

A POI popularity derivation device (10) includes: a dictionary generation unit (11) that assigns a feature word used as a co-occurrence word of a POI name to each popularity-assigned POI name serving as a popularity assignment target to generate a popularity-assigned POI dictionary in which a popularity-assigned POI name and a feature word are associated with each other; an extraction unit (12) that extracts posted data serving as a search target from posted data on the basis of predetermined criteria; and a popularity derivation unit (18) that searches for the posted data on the basis of a predetermined rule regarding feature words while referring to the popularity-assigned POI dictionary, to extract posted data linked to the popularity-assigned POI name, and derives the popularity of each popularity-assigned POI name on the basis of the number of pieces of extracted posted data for each popularity-assigned POI name.

A network communications environment has advanced interaction management functionality that enables the opportunities for users to interact with one another to be expanded beyond the opportunities that exist with closed interaction management approaches while still providing reasonable privacy and security protections that are not available with open interaction management approaches. The advanced interaction management functionality also encourages the proliferation of trust-based interactions.

A network communications environment has advanced interaction management functionality that enables the opportunities for users to interact with one another to be expanded beyond the opportunities that exist with closed interaction management approaches while still providing reasonable privacy and security protections that are not available with open interaction management approaches. The advanced interaction management functionality also encourages the proliferation of trust-based interactions.

A method of presenting video information to a user includes obtaining a comparison video including a plurality of frames from a social media platform, determining a presence of at least one event in the comparison video, obtaining social media metrics for the comparison video from the social media platform, and assigning a social media value to the at least one event based at least partially on the social media metrics and at least one property of the event.

A method of presenting video information to a user includes obtaining a comparison video including a plurality of frames from a social media platform, determining a presence of at least one event in the comparison video, obtaining social media metrics for the comparison video from the social media platform, and assigning a social media value to the at least one event based at least partially on the social media metrics and at least one property of the event.

An event query host can include one or more processors configured to process an event stream indicating events that occurred on one or more computing devices. The event stream comprises event data that is associated with occurrences of events on the one or more computing devices. The event query host can forward the event data to a first query engine and to a second query engine. The first query engine can determine, based on a set of query definitions, that the forwarded event data is associated with a first query to be executed by the first query engine, and so executes the first query instance associated with the first query. The second query engine can also determine, based on the set of query definitions, that the forwarded event data is associated with a second query to be executed by the second query engine, and so executes the second query instance associated with the second query.

An event query host can include one or more processors configured to process an event stream indicating events that occurred on one or more computing devices. The event stream comprises event data that is associated with occurrences of events on the one or more computing devices. The event query host can forward the event data to a first query engine and to a second query engine. The first query engine can determine, based on a set of query definitions, that the forwarded event data is associated with a first query to be executed by the first query engine, and so executes the first query instance associated with the first query. The second query engine can also determine, based on the set of query definitions, that the forwarded event data is associated with a second query to be executed by the second query engine, and so executes the second query instance associated with the second query.